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Scala for Data Science

You're reading from   Scala for Data Science Leverage the power of Scala with different tools to build scalable, robust data science applications

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Product type Paperback
Published in Jan 2016
Publisher
ISBN-13 9781785281372
Length 416 pages
Edition 1st Edition
Languages
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Author (1):
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Pascal Bugnion Pascal Bugnion
Author Profile Icon Pascal Bugnion
Pascal Bugnion
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Toc

Table of Contents (17) Chapters Close

Preface 1. Scala and Data Science FREE CHAPTER 2. Manipulating Data with Breeze 3. Plotting with breeze-viz 4. Parallel Collections and Futures 5. Scala and SQL through JDBC 6. Slick – A Functional Interface for SQL 7. Web APIs 8. Scala and MongoDB 9. Concurrency with Akka 10. Distributed Batch Processing with Spark 11. Spark SQL and DataFrames 12. Distributed Machine Learning with MLlib 13. Web APIs with Play 14. Visualization with D3 and the Play Framework A. Pattern Matching and Extractors Index

Bootstrapping the applications

When we linked require.js to our application, we told it to use main.js as our entry point. To test that this works, let's start by entering a dummy main.js. JavaScript files in Play applications go in /public/javascripts:

// public/javascripts/main.js

require([], function() {
  console.log("hello, JavaScript"); 
});

To verify that this worked, head to 127.0.0.1:9000 and open the browser console. You should see "hello, JavaScript" in the console.

Let's now write a more useful main.js. We will start by configuring RequireJS, giving it the location of modules we will use in our application. Unfortunately, NVD3, the graph library that we use, does not play very well with RequireJS so we have to use an ugly hack to make it work. This complicates our main.js file somewhat:

// public/javascripts/main.js

(function (requirejs) {
  'use strict';

  // -- RequireJS config --
  requirejs.config({
    // path to the web jars. These...
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